What actually breaks when AI agents scale?

Over the last few months watching teams deploy AI agents across clients, I’m seeing a few consistent patterns:

1️⃣ Prompt stacks grow messy fast.

What started as 10 prompts becomes 60+. Small edits compound. Nobody remembers why decisions were made.

2️⃣ Drift is invisible.

Agents don’t “learn” — they just execute. But outputs slowly degrade when context changes.

3️⃣ Customization kills reuse.

Every client tweak forks the system. Soon you’re maintaining 5 slightly different versions.

4️⃣ No audit layer.

When something goes wrong, nobody knows:

– what version ran

– what context it had

– why it decided what it did

At small scale this is fine.

At enterprise scale, it becomes governance.

Curious:

For those running multi-agent systems or client-facing agents —

What’s the first thing that started to hurt as you scaled?

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